Optimization of physical gate parameters is required for constructing fault-tolerant quantum computers. Characterization methods such as randomized benchmarking or tomography require interruption of necessary error detection operations and do not guarantee optimal performance in an error correction circuit. Optimization of physical gate parameters using error model optimization methods requires that an error model is trained, such that measured physical errors may be linked to physical gates, and requires that the determined errors are linked back to changes in control parameters, increasing the complexity of the optimization process.